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1.
Bernstein Polynomial Estimation in the Partially Linear Model under Monotonicity Constraints
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In this paper, a Bernstein-polynomial-based likelihood
method is proposed for the partially linear model under monotonicity constraints.
Monotone Bernstein polynomials are employed to approximate the monotone
nonparametric function in the model. The estimator of the regression parameter
is shown to be asymptotically normal and efficient, and the rate of convergence
of the estimator of the nonparametric component is established, which could be
the optimal under the smooth assumptions. A simulation study and a real data
analysis are conducted to evaluate the finite sample performance of the proposed
method. 相似文献
2.
We propose randomized inference(RI), a new statistical inference approach. RI may be realized through a randomized estimate(RE) of a parameter vector, which is a random vector that takes values in the parameter space with a probability density function(PDF) that depends on the sample or sufficient statistics,such as the posterior distributions in Bayesian inference. Based on the PDF of an RE of an unknown parameter,we propose a framework for both the vertical density representation(VDR) test and the construction of a confidence region. This approach is explained with the aid of examples. For the equality hypothesis of multiple normal means without the condition of variance homogeneity, we present an exact VDR test, which is shown as an extension of one-way analysis of variance(ANOVA). In the case of two populations, the PDF of the Welch statistics is given by using the RE. Furthermore, through simulations, we show that the empirical distribution function, the approximated t, and the RE distribution function of Welch statistics are almost equal. The VDR test of the homogeneity of variance is shown to be more efficient than both the Bartlett test and the revised Bartlett test. Finally, we discuss the prospects of RI. 相似文献
3.
张忠占 《应用数学学报(英文版)》2001,17(4):457-468
1. IntroductionConsider a follow-up study which is carried out to investigate the association betweenexposure variables and mortality rate in a cohort. In the case where the cohort is of 1argesise, the complete follow-up ndght be too expensive or difficult, and various nested samplingmethod8 have been suggested by Thomas[l], Prenti..[2] 5 Goldstein and Langholzl'] and otherauthors. Most of the authors employ Coxl4] regression mode1 for estimating the hazard ratio8of exposures.Now a well-reco… 相似文献
4.
5.
张忠占 《高校应用数学学报(A辑)》1993,(4):386-395
本文讨论了三参数对正态分布的参数估计问题,指出了最小二乘估计存在的问题,并提出了两种修改的最小二乘估计,模拟研究表明,这种估计较以前提出的估计有一定的优点。 相似文献
6.
ZhangZhongzhan LiGuoying 《高校应用数学学报(英文版)》1999,14(2):158-168
This paper presents an estimator of location vector based on one-dimensional projection of high dimensional data. The properties of the new estimator including consistency ,asymptotic normality and robustness are discussed. It is proved that the estimator is not only stronglyconsistent and asymptotically normal but also with a breakdown point 1/2 and a bounded influence function. 相似文献
7.
在恒定应力部分加速寿命试验下,基于左删失样本,研究了逆Rayleigh分布的参数估计问题。运用极大似然法得到未知参数和加速因子的极大似然估计。根据Louis提出的缺失信息原则,计算了Fisher信息矩阵,得到分布参数和加速因子的近似置信区间。当取参数的先验分布为指数分布时,在平方损失函数下求得了参数的贝叶斯估计,并利用极大似然法估计了超参数。通过 Monte Carlo 模拟,得到估计量的均方误差,据此对极大似然估计和贝叶斯估计进行了比较。最后,计算了不同左删失样本下逆Rayleigh分布参数及加速因子的估计。 相似文献
8.
参数的变换矩估计方法 总被引:2,自引:0,他引:2
概率积分变换是统计分析中一个重要方法,它在拟合优度检验中得到了良好的应用.本文将这一思想应用于参数估计问题,提出一般参数估计的变换矩方法研究表明,这种方法既有较高的效率,又有良好的稳健性.文中证明了变换矩估计的存在性,相合性和渐近正态性,同时给出了影响函数和崩溃点,并具体分析了几个常见分布族的结果. 相似文献
9.
n this paper, we propose composite quantile regression for functional linear model with dependent data, in which the errors are from a short-range dependent and strictly stationary linear process. The functional principal component analysis is employed to approximate the slope function and the functional predictive variable respectively to construct an estimator of the slope function, and the convergence rate of the estimator is obtained under some regularity conditions. Simulation studies and a real data analysis are presented for illustration of the performance of the proposed estimator. 相似文献
10.
??n this paper, we propose composite quantile regression for functional linear model with dependent data, in which the errors are from a short-range dependent and strictly stationary linear process. The functional principal component analysis is employed to approximate the slope function and the functional predictive variable respectively to construct an estimator of the slope function, and the convergence rate of the estimator is obtained under some regularity conditions. Simulation studies and a real data analysis are presented for illustration of the performance of the proposed estimator. 相似文献